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- Relationship between poisson and exponential distribution
Note, that a poisson distribution does not automatically imply an exponential pdf for waiting times between events This only accounts for situations in which you know that a poisson process is at work But you'd need to prove the existence of the poisson distribution AND the existence of an exponential pdf to show that a poisson process is a suitable model!
- probability - Distribution of Event Times in a Poisson Process . . .
Normally, everyone talks about the distribution of interarrival times in a Poisson Process are Exponential but what about the distribution of the actual event times?
- Why is Poisson regression used for count data? - Cross Validated
Poisson distributed data is intrinsically integer-valued, which makes sense for count data Ordinary Least Squares (OLS, which you call "linear regression") assumes that true values are normally distributed around the expected value and can take any real value, positive or negative, integer or fractional, whatever Finally, logistic regression only works for data that is 0-1-valued (TRUE-FALSE
- How to Choose Poisson Time Interval - Cross Validated
A Poisson process is one where mean = var = λ How do you decide what time interval fulfills these criteria when fitting the Poisson distribution to a process? Can all processes be modeled as Poisson
- Checking if two Poisson samples have the same mean
This is an elementary question, but I wasn't able to find the answer I have two measurements: n1 events in time t1 and n2 events in time t2, both produced (say) by Poisson processes with possibly-
- r - Rule of thumb for deciding between Poisson and negative binomal . . .
The Poisson distribution implies so a one-sample test can provide a P -value for testing Poisson vs negative binomial Another test for equidispersion is the Lagrange Multiplier which follows a one-degree distribution under the null
- Finding the probability of time between two events for a poisson process
The logic here seems obvious: The probability of a given wait time for independent events following a poisson process is determined by the exponential probability distribution $\lambda e^ {-\lambda x}$ with $\lambda = 0 556$ (determined above), so the area under this density curve (the cumulative probability) is 1
- Poisson or quasi poisson in a regression with count data and . . .
I have count data (demand offer analysis with counting number of customers, depending on - possibly - many factors) I tried a linear regression with normal errors, but my QQ-plot is not really goo
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